GeoTime Retrieval through Passage-based Learning to Rank

Geo-temporal Information Retrieval Based on Semantic Role Labeling and Rank Aggregation

The purpose of this study is to improve effectiveness of geotemporal information retrieval with semantic role labeling (SRL) for sentences in topics and documents, especially focusing on locational and temporal facets. We propose a combination of four language models (LM) representing different semantic roles and scopes of models for documents and a rank aggregation method. The rationale is bas...

Question Answering and Multi-search Engines in Geo-Temporal Information Retrieval

In this paper we present a complete system for the treatment of both geographical and temporal dimensions in text and its application to information retrieval. This system has been evaluated in both the GeoTime task of the 8th and 9th NTCIR workshop in the years 2010 and 2011 respectively, making it possible to compare the system to contemporary approaches to the topic. In order to participate ...

Boosting Passage Retrieval through Reuse in Question Answering

Question Answering (QA) is an emerging important field in Information Retrieval. In a QA system the archive of previous questions asked from the system makes a collection full of useful factual nuggets. This paper makes an initial attempt to investigate the reuse of facts contained in the archive of previous questions to help and gain performance in answering future related factoid questions. I...

ارائه الگوریتمی مبتنی بر یادگیری جمعی به منظور یادگیری رتبه‌بندی در بازیابی اطلاعات

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by two systems: a learning system and a ranking system. The learning system takes training data as input and constructs a ranking ...

Text Retrieval Baseline for NTCIR-GeoTime